Multi-camera imaging systems

ABSTRACT

A plurality of multi-camera systems and methods for imaging faint objects are disclosed, which includes an array of cameras that, when taken alone, are incapable of imaging such objects. The systems and methods may include common field arrays, hybrid field arrays, and/or adaptive field arrays.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Phase Patent Application, and claimspriority to and the benefit of International Application Serial No.PCT/US17/52105, filed on Sep. 18, 2017, which claims priority to and thebenefit of U.S. Provisional Patent Application No. 62/396,780, filedSep. 19, 2016, entitled “MULTI-CAMERA IMAGING SYSTEMS,” the entirecontents of both of which are incorporated herein by reference.

GOVERNMENT LICENSE RIGHTS

This invention was made in part with government support under contractnos. FA9451-14-M-0183 and FA9451-16-C-0406 awarded by the DefenseContract Management Agency. The government has certain rights in theinvention.

BACKGROUND Technical Field

The present invention relates to multi-camera imaging systems, and moreparticularly, to systems and methods for imaging faint objects with anarray of cameras that, when taken alone, are incapable of imaging suchobjects and/or are considered economical or low-performance.

Description of the Related Art

Limitations on previously existing imaging systems have made imagingobjects (e.g., faint objects) in space and other low-light orlow-contrast environments a challenge. The sensitivity of a camera isrelated by the signal-to-noise ratio (SNR) of the image data product. Inthe context of previously existing imaging systems, the usefulness ofany given camera is directly limited by the signal-to-noise ratio it iscapable of producing under a given condition.

One mechanism for increasing a camera's ability to produce highersignal-to-noise ratios is by designing the system with larger apertures.Typically, cameras operating with larger apertures would sacrifice thefield of view (FOV), due to a finite limit on the size of the focalplane detecting system. Camera systems capable of providing high SNRratios typically have narrow FOV (e.g., telescopes used forastrophotography). As a result, they are often useless for detectingobjects (e.g., faint objects) in space or other low-light environmentswhen the location of a target object is non-static, unpredictable,and/or unknown. Since the FOV is so small, the probability ofserendipitously having a target appear in the FOV and thus be detectableis likely very small, unless the position of the target is foreknown towithin a certainty of the size of the small FOV. Moreover, existingcamera systems capable of producing very high SNR are expensive (e.g.,ranging from $100,000 to $100 million or more), due to the cost ofdesigning and precision fabrication of large apertures, complex lenses,advanced image sensors and their cooling systems, and other high-endcomponents. As an example, the DARPA Space Surveillance Telescope (SST),which initially deployed for testing and evaluation in New Mexico, andwhich was expected to become operational in 2016, is a large, custommonolithic telescope costing over $90 million with an FOV of severaldegrees. While the DARPA SST may be capable of seeing dim space objects,and has a relatively wide FOV in comparison to telescopes of similaraperture diameter, its FOV is still small and inadequate. That isbecause, fundamentally, a very large telescope (e.g., over 1 m) can onlycreate an image of several degrees in FOV. And to do so, the SST andother similar systems require making disadvantageously large focal planedetection systems virtually by mosaicking many imaging detectors intothe imaging plane of a single monolithic aperture. In the SST, each ofthese mosaicked focal planes then stares at a slightly different pieceof the sky; however, each detector stares through the same monolithicaperture. The SST is not able to use multiple separate imaging systemsto exploit the economy of scale or the mass factory-produced andcommodity optics. Thus, the known imaging systems, such as the DARPASST, that rely on the use of several such cameras with the hopefulobjective of catching and sufficiently imaging such a target objectwithin a broad composite field of view, are impractical andprohibitively expensive in personal and commercial settings alike.

SUMMARY

As described and illustrated by way of one or more embodiments,multi-camera systems for imaging objects (e.g., faint objects) areprovided (e.g., systems and methods for imaging faint objects with anarray of cameras that, when taken alone, are incapable of imaging suchobjects and/or are considered economical or low-performance by those ofordinary skill in the art at the time of the present application'sfiling). The systems include, by way of example, common field arrays,hybrid field arrays, and/or adaptive field arrays.

Common Field Array

In one or more embodiments, an imaging system includes a plurality ofcameras. Each of the cameras is configured to capture an image and has afield of view defining the boundaries of the capturable image. Thecameras are configured, adapted, positioned, oriented, arranged, ordisposed such that a field of view region is common among the fields ofview of the cameras. The imaging system may include, for example, atleast five cameras, and the field of view region may be common among atleast five of the fields of view of the cameras. The field of viewregion may be common among all of the fields of view of all of thecameras in the imaging system.

The imaging system includes a computing system communicatively coupledto the cameras. Through the execution of instructions by one or moreprocessors, the computing system is configured to access an imagecaptured by each of the cameras and to co-register the images. Thecomputing system is further configured to combine the images, therebygenerating a combined image having a greater signal-to-noise ratio thanany of the individual images standing alone. The signal-to-noise ratioof some or all of the combined image may be at least √{square root over(T)} times greater than the signal-to-noise ratio of at least one of theindividual images standing alone, where N is the quantity of individualimages combined.

Hybrid Field Array

In one or more embodiments, an imaging system includes a plurality ofcamera arrays. Each camera array includes a plurality of cameras eachconfigured to capture an image and having a field of view defining theboundaries of the capturable image. The cameras of each camera array areconfigured, adapted, positioned, oriented, arranged, or disposed suchthat the camera array has a common field of view region that is commonamong the fields of view of the cameras. Each camera array may include,for example, at least five cameras, and the common field of view regionmay be common among at least five of the fields of view of the cameras.The common field of view region may be common among all of the fields ofview of all of the cameras in each array. The common field of viewregion of each respective camera array partially overlaps the commonfield of view region of another of the plurality of camera arrays.

The imaging system includes a computing system communicatively coupledto the camera arrays. The computing system is configured to generatefrom a plurality of captured images of each camera array a combinedimage having a greater signal-to-noise ratio than any of the imagescaptured by any individual camera of each respective camera array.Through the execution of instructions by one or more processors, thecomputing system is configured to stitch the combined images togetherbased on the spatial relationship in which the common field of viewregion of each respective camera array partially overlaps the commonfield of view region of another of the camera arrays. Thus, the imagingsystem generates a composite image representing a wider field of viewthan any combined image standing alone and having a greatersignal-to-noise ratio than any individual image captured by anyindividual camera of the camera arrays. The signal-to-noise ratio ofsome or all of the composite image may be at least √{square root over(N)} times greater than the signal-to-noise ratio of at least one of theindividual images captured by one of the individual cameras within oneof the camera arrays, where N is the quantity of individual imagescombined.

Adaptive Field Array

In one or more embodiments, an imaging system includes a plurality ofcameras. Each camera is configured to capture an image and has a fieldof view defining the boundaries of the capturable image. The pluralityof cameras are configured, adapted, positioned, oriented, arranged, ordisposed such that the fields of view of the cameras collectively form acomposite field of view that is greater than any of the fields of viewof any of the individual cameras within the plurality of cameras.

The imaging system includes a computing system communicatively coupledto the cameras. The computing system is configured to detect a targetobject in an image captured by one of the cameras and, in response todetecting the target object, automatically reconfigure, readapt,reposition, reorient, or rearrange the plurality of cameras to produce acommon field of view region that is common among the fields of view ofthe cameras and contains the detected target object. The computingsystem is further configured to access an image captured by each of thecameras after being reconfigured, readapted, repositioned, reoriented,or rearranged. The computing system is configured to co-register theimages and then combine the images, thereby generating a combined imagehaving a greater signal-to-noise ratio than any of the individual imagesstanding alone. The signal-to-noise ratio of some or all of the combinedimage may be at least √{square root over (N)} times greater than thesignal-to-noise ratio of at least one of the individual images standingalone, where N is the quantity of individual images combined.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is, in accordance with one or more embodiments, a block diagramof an exemplary imaging system.

FIG. 2 illustrates, in accordance with one or more embodiments, a commonfield array of an exemplary imaging system.

FIG. 3 illustrates, according to one or more embodiments, a field ofview region that is common among the fields of view of the plurality ofcameras of an exemplary imaging system.

FIG. 4 illustrates, in accordance with one or more embodiments, aco-registration operation that a computing device of an exemplaryimaging system is configured to perform.

FIG. 5 illustrates, in accordance with one or more embodiments, a hybridfield array of an exemplary imaging system.

FIG. 6 illustrates, in accordance with one or more embodiments, anadaptive field array of an exemplary imaging system.

FIG. 7 illustrates, in accordance with one or more embodiments, anexemplary camera calibration process.

FIG. 8 illustrates, in accordance with one or more embodiments, anexemplary data collection process.

FIG. 9 illustrates, in accordance with one or more embodiments, anexemplary data reduction process.

DETAILED DESCRIPTION

As described and illustrated by way of one or more embodiments,multi-camera systems for imaging objects (e.g., faint objects) areprovided (e.g., systems and methods for imaging faint objects with anarray of cameras that, when taken alone, are incapable of imaging suchobjects and/or are considered economical or low-performance by those ofordinary skill in the art at the time of the present application'sfiling). The one or more embodiments are described for illustrative(i.e., explanatory) purposes only and are not, nor should they beconstrued as being, exhaustive or otherwise limited to the precise formsillustrated and described. Rather, as those of ordinary skill in the artwill readily recognize and appreciate in view of the teachings in thisapplication, additional embodiments and variations are possible in lightof, and contemplated by, such teachings. As used in this disclosure, theterm “exemplary” means one of many possible non-limiting examplesprovided for purposes of explanation and illustration. As used herein,the term “exemplary” does not mean preferable, optimal, or ideal, anddoes not mean that the presence of any elements, components, or stepspresent in any subject matter referenced as “exemplary” are necessary orrequired in other possible embodiments or variations of the referencedsubject matter. As used herein, the articles “a” and “an” mean one ormore unless otherwise stated. For purposes of this disclosure, the terms“comprises,” “comprising,” “includes,” and “including” all meanincluding, but not limited to, the items, elements, components, or stepslisted.

The imaging systems and methods described herein provide numeroustechnological advances and benefits over previously existingtechnologies for imaging objects (e.g., faint objects). Such advancesand benefits include, for example, the ability to detect and imageobjects (e.g., faint objects) in space or other low-light environmentsusing cameras that, when taken alone, are incapable of imaging suchobjects and/or are considered economical or low-performance by those ofordinary skill in the art at the time of the present application'sfiling (e.g., because they have an insufficient signal-to-noise ratio).

In one or more embodiments, the imaging systems and methods describedherein achieve such advances and benefits by combining co-registeredimages captured by individual cameras (e.g., within a camera array) togenerate a combined image having a greater signal-to-noise ratio thanany of the individual images standing alone. For purposes of thisdisclosure, the terms “combine,” “combined,” and “combining” mean add,sum, stack, superimpose, fuse, or integrate through the use of one ormore statistical operations, in addition to other meanings understood bythose of ordinary skill in the art in view of the foregoing illustrativemeanings and the context in which the terms are used. In one or moreembodiments, the signal-to-noise ratio of some or all of the combinedimage (depending on the size of the common field of view region comparedto the size of the individual field of view of each camera) is at least√{square root over (N)} times greater than the signal-to-noise ratio ofat least one of the images standing alone, where N is the quantity ofindividual images combined.

The ability to avoid using individual high-performance or highlysensitive cameras (e.g., those capable of imaging low-light targets withhigher signal-to-noise ratios, such as imaging wide to ultra-wide fieldsfrom about 20 degrees to about 180 degrees angle of view, in less than0.01 Lux, or for targets or stars with greater than 12 MV stellarmagnitudes) provides numerous benefits. Such benefits include, forexample, the ability to lower system construction costs (e.g., theinitial capital invested in the cameras), operational costs (e.g., thecost of the electricity required to power the system, which also reducesthe environmental footprint), and maintenance and repair costs (e.g.,the cost of replacing a camera, which may be readily available or“off-the-shelf” as opposed to more expensive and/or more difficult toacquire high-performance cameras). Relatedly, the imaging system alsoallows users to achieve the foregoing enhancement in image sensitivitywithout having to alter the optics of the individual cameras (e.g., byreplacing the camera lenses to achieve a different focal length).

Other advances and benefits provided by the imaging systems and methodsdescribed herein include a reduction in image artifacts caused by cosmicrays, a substantially increased bit depth that achieves the magnitude ofdata fidelity required to perform advanced deconvolution operations, andenhancements in scalability (e.g., the ability to scale, add, or removecameras as needed to achieve a desired image sensitivity and/or bitdepth) and flexibility (e.g., the ability to reconfigure the camerasfrom a single-array configuration into a plurality of sub-arraysconfigurations to image multiple target objects).

One or more embodiments of the imaging systems and methods may be scaledto achieve the desired sensitivity by understanding the sensitivity of asingle camera system. The sensitivity of a common field array of several(N) of these camera systems may be described as the square root of N.Thus, if a single camera system is capable of producing a useful SNR,such as SNR=3, on a target of stellar magnitude Mv 12, then a systemthat is capable of producing a useful SNR on a much dimmer target ofstellar magnitude Mv 14 would need an array of approximately 40 of thesecamera systems. It was found through radiometric mathematics of stellarmagnitude and the square root of N improvement that it takes roughly6.25 cameras being co-added simultaneously to achieve a single Mvincrease in sensitivity. Thus, preferably, in one or more embodiments,6.25 squared number of cameras may be used to achieve a 2 Mv increase insensitivity.

In one or more embodiments, the cameras may be easily added or removedfrom the array to tune it, reuse spares or sacrifice failed/failingcameras from the array since each camera produces an independent,realized estimated measurement of the same scene. The array is robustand allows for adding or subtracting an arbitrary number of cameraswithout significantly sacrificing spatial resolution or FOV; adding orsubtracting the cameras primarily results in changing the sensitivity ofthe array only.

In one or more embodiments, the array construct is also flexible in thatthe array may be partitioned into a group of “sub-arrays,” where eachsub-array may include some number of cameras that are all pointed, in a“common field sense,” at the same portion of the FOV. Yet each sub-arraycould be independently steered, i.e., if each sub-array were mounted onan independent pan-tilt actuator or on a gimballed pointing system ofits own. In this way, all sub-arrays could be pointed solely at the“common field,” e.g., at the same portion of the FOV, or they could beindependently steered to dynamically change each FOV and/or the pointingdirection, independently from each other sub-array, albeit suchindependently steered sub-array would be less sensitive than the entirearray when considered by itself, separate from the complete array bymeans of pointing at a different location.

The many technical advances and benefits provided by the systemsillustrated and described herein may be employed in any number ofpossible applications, including astrophotography, deep space searching,geosynchronous and non-geosynchronous satellite detection and tracking,tactical sensing, night vision (e.g., for military and/or lawenforcement applications), and other applications that those of ordinaryskill in the art will recognize and appreciate in view of the presentteachings.

Contrast-challenged targets are targets that may have a large amount ofsignals, but are presented in clutter or background that also similarlyhas many targets being present. The difference between the target signaland the background or clutter signal might be very small and require avery highly resolved measurement of the scene in order to discern thefine, subtle variations between the background and the target. Since oneor more embodiments of the common field array approach (see below)yields an imaging bit depth that approaches Log 2(N) increase in bitdepth, one or more embodiments of the common field array approach may beused to increase the effective bit-depth of an imaging system. Forinstance, if a very well-designed, well-engineered, high bit-depthsensor has 16 bits of dynamic range “depth” to resolve variations in thefield (that is, 2{circumflex over ( )}16 levels of brightness todiscriminate changing scene features), then 128 of the same sensitivesystems arranged in a common field array implementation would be able toproduce 128*(2{circumflex over ( )}16-1) levels of brightness todiscriminate the fine, subtle contrast between a target and itsbackground/clutter. This is approaching Log 2(128)=7 enhancement to thebit depth of the system, or approaching 23 bits depth to images producedat the same frame rate and exposure time, using the artful construct ofan array, using many of the same systems.

Common Field Array

FIG. 1 is a block diagram of an exemplary imaging system in accordancewith one or more embodiments. As illustrated in FIG. 1, the imagingsystem 100 includes a plurality of imaging devices (e.g., cameras) 101which, as discussed below, may form various arrays. Illustratively, thecameras may be Basler ACA2040-25 gm-NIR, Basler ACA4112-8 gm or similarofferings, coupled to 1″, 1.1″ format compatible camera lenses for 2camera detection systems cited herein, such as Nikon 10.5 mm F/2.8fisheye lenses, Voigtlander 10.5 mm F/0.95 lenses and/or Kowa 6 mm F/1.8HC-series ultraWide FOV (UWFOV) lenses. Other narrower FOV lenses havebeen tested and shown to be effective, out to 500 mm F/5.0 35 mm formatlenses and 35 mm cameras, together capable of providing up to 5 degreesdiagonal Angle of View (FOV). Other cameras might include smaller orlarger physical formats, such as the 35 mm cameras that have been testedfor the arrays of one or more embodiments, and require proper lenses toachieve the proper imaging performance suited to each camera. It hasbeen found that using digital compression, such as the currentlyextremely popular H.264 video coding schemes, may have deleteriouseffects on the ability to reliably achieve square root of N enhancement.Thus, preferably, cameras should be chosen to best present “raw,”unfiltered, uncompressed detection data, as detecting dim targets andthe statistical augmentation relies especially upon the subtle signalshidden in the smallest signal detection changes.

For purposes of this disclosure, the term “camera” includes an imagesensor (a pixel sensor, for example) that may comprise a charge-coupleddevice (CCD), complementary metal oxide semiconductor sensor (CMOS), orany other image sensing device that receives light and generates imagedata in response to the received image. The imaging device also includesa lens through which light is introduced to the image sensor. In someembodiments, each lens may be a lens assembly that may comprise one ormore lens units and an actuator for moving the lens among a number ofdifferent lens positions. The actuator may be controlled by a driver.The plurality of cameras may be communicatively coupled to a switch 102or other multiplexing device compatible with the digital interface ofeach camera (e.g., an Ethernet switch) through any of a variety ofpossible data transport links and/or camera interfaces 103 (e.g., aGigabit Ethernet (GigE) interface, a Universal Serial Bus 3 (USB3)interface, a CameraLink interface, or any other camera interfacerecognized as suitable by those of ordinary skill in the art).Illustratively, the switch used in one or more embodiments may be CiscoSG-500X-48P, as an example of many existing commodity switches that areaffordable and suitable for our purposes. For example, one or moreembodiments may employ many (e.g., up to 48) 1 GigE links between theswitch and each camera, and up to 4× of the faster 10GigE SFP+ portsbetween the switch and the master computer, albeit the exactarchitecture can be different, tailored to the frame rate and bandwidthrequirements of each system realization.

The imaging system further includes a computing system 104communicatively coupled to the switch or other multiplexing device. Thecomputing system includes a computing device 105 including one or moreprocessors, a communications network interface and/or other types ofinterfaces 106, a memory 107 (one or more memory devices such as RandomAccess Memory, nonvolatile memory, etc.), and executable instructions108 stored in the memory that, when executed, cause the processor toperform a variety of functions (e.g., co-registering images, combiningimages, stitching images, deconvolving images, and/or other imageprocessing and mathematical operations). The computing device may be apart of a networked system, and some of the embodiments of the inventionmay be practiced in a distributed computing environment in which thefunctions are performed by remote processing devices that are linkedthrough the communications network. In a distributed computingenvironment, the executable functions may be located in the local andremote memory devices. In other embodiments, the computer device may bea standalone device. The computer device is any computerized devicehaving at least one processor. As illustrated in FIG. 1, in one or moreembodiments the computing device may be communicatively coupled to aglobal positioning system (GPS) 109. The GPS may provide coordinate orother geographical data to the computing device for purposes offacilitating co-registration and/or other operations. Illustratively,the GPS used in one or more embodiments may be the SpectracomTSYNC-PCIE.

Although the block diagram of FIG. 1 depicts certain components andconnections for illustrative purposes, those of ordinary skill in theart should readily understand and appreciate that other possiblecomponents and connections are possible in light of, and contemplatedby, the present teachings. Similarly, although the block diagram of FIG.1 depicts a single imaging system, those of ordinary skill in the artshould readily understand and appreciate that a plurality of suchimaging systems 100 a-n may be employed in a networked or otherwiseconnected fashion in which the systems communicate directly with oneanother or through a central computerized control system 110.

FIG. 2 illustrates, in accordance with one or more embodiments, anexemplary common field array 201 of an imaging system 100. For purposesof this disclosure, the term “common” does not mean ordinary,unsophisticated, or frequently occurring, but rather means defining thesame spatial region, superimposed, joint, shared, stacked, and othermeanings understood by those of ordinary skill in the art in view of theforegoing illustrative meanings and the context in which the term isused in this application. As illustrated in FIG. 2, in one or moreembodiments an imaging system 100 includes a plurality of cameras eachconfigured to capture an image. For purposes of this disclosure, theterm “image” means a digital image, a digital representation of animage, and/or digital data representing an image, including, forexample, raster type images, vector type images, and other forms ofdigital images appreciated by those of ordinary skill in the art in viewof the foregoing examples.

The cameras 101 may be monolithic, cooled or uncooled 2-D area camerasof CMOS, CCD, Avalanche Photodiode Arrays, linear or multi-lineararrays, scanned in 1-dimension of either direct or time-delayintegration readout constructs; single or multi-element 2 directionallyscanned sensors or other cameras recognized as suitable by those ofordinary skill in the art. Each of the cameras has a field of view 202that defines the boundaries of a capturable image. The field of view ofeach camera may be any angle recognized as suitable by those of ordinaryskill in the art. In one or more embodiments, for example, the field ofview may range from about 0.15 square arc-minutes to about 30 squaredegrees, or from about 1-degree horizontal to about 180 degreeshorizontal.

In one or more embodiments, the plurality of cameras 101 are configured,adapted, positioned, oriented, arranged, or disposed such that a fieldof view region is common among the fields of view of the plurality ofcameras (common field of view region 203). In one or more embodiments,the elegance and simplicity of changing the number of cameras in acommon field array is that the system can be expanded or collapsed tosmaller or greater number of cameras with almost no hardware,architectural, or design changes. The software system will recognize thenumber of cameras in the array and scale the final imaging product toinclude all useful information sources. As the greater number of systemsstart to saturate the amount of data or the speed at which a singlemaster computer can handle many camera data feeds coming into it, ahierarchical pyramid of arrays of multiple common field arrays may becreated, i.e., a common field array of common field arrays. Forinstance, a single computer and network switch may usefully integrate atleast 20 cameras and up to 48 cameras. Preferably, a single computer andtwo network switches may accommodate up to 96 cameras in one or moreembodiments of the array. Thus, to create an array of 960 cameras, onemay assemble 10×96 camera arrays and have each computer that handles 96cameras to create a single image product. The foregoing process may bereplicated 10 times and fed into another common field array mastercomputer that treats each 96-camera array as if it were a single camera,co-registering them together and combining their images into,ultimately, a 960-camera array (of arrays).

The imaging system may include any number of cameras as dictated by thedesired imaging objective. FIG. 3 illustrates, according to one or moreembodiments, a field of view region that is common among the fields ofview of the plurality of cameras in an exemplary imaging system (commonfield of view region 203). As illustrated in FIG. 3, in one or moreembodiments, the imaging system 100 may include at least four cameras,and the common field of view region 203 may be common among at leastfour of the fields of view of the cameras. In one or more embodiments,the imaging system 100 may include at least five cameras, and the commonfield of view region 203 may be common among at least five of the fieldsof view of the cameras. In one or more embodiments, the imaging system100 may include at least six cameras, and the common field of viewregion 203 may be common among at least six of the fields of view of thecameras. In one or more embodiments, the field of view region 203 iscommon among all of the fields of view of all of the cameras (see, e.g.,the common field arrays illustrated in FIGS. 2 and 3).

The field of view region 203 that is common among the fields of view ofthe cameras may vary in size depending on the fields of view of thecameras and the manner in which the cameras are configured, adapted,positioned, oriented, arranged, or disposed. Thus, the common field ofview region 203 may overlap portions of each field of view of theindividual cameras (e.g., 301 through 304, as illustrated in FIG. 3), orthe common field of view region 203 may overlap all or substantially allof each field of view of the individual cameras (as illustrated in FIG.2). In one or more embodiments, all of the cameras may be pointed in thesame or substantially the same direction. The cameras may,alternatively, be pointed in different directions with respect to oneanother as required to produce the common field of view region among thefields of view of the cameras.

By way of example, the plurality of cameras may include a first camerahaving a first field of view, a second camera having a second field ofview, and a third camera having a third field of view. The cameras maybe configured, adapted, positioned, oriented, arranged, or disposed suchthat at least a portion of the field of view of the first camera definesthe same spatial region as a portion of the field of view of the secondcamera, thereby creating a field of view region that is common betweenthe first camera and the second camera. The cameras may further beconfigured, adapted, positioned, oriented, arranged, or disposed suchthat at least a portion of the field of view of the third camera definesthe same spatial region as the field of view region that is commonbetween the first camera and the second camera, thereby creating a fieldof view region that is common between the first camera, the secondcamera, and the third camera.

In one or more embodiments, the plurality of cameras may further includea fourth camera having a fourth field of view. The cameras may befurther configured, adapted, positioned, oriented, arranged, or disposedsuch that at least a portion of the field of view of the fourth cameradefines the same spatial region as the field of view region that iscommon between the first camera, the second camera, and the thirdcamera, thereby creating a field of view region that is common betweenthe first camera, the second camera, the third camera, and the fourthcamera.

In one or more embodiments, the plurality of cameras may further includea fifth camera having a fifth field of view. The cameras may be furtherconfigured, adapted, positioned, oriented, arranged, or disposed suchthat at least a portion of the field of view of the fifth camera definesthe same spatial region as the field of view region that is commonbetween the first camera, the second camera, the third camera, and thefourth camera, thereby creating a field of view region that is commonbetween the first camera, the second camera, the third camera, thefourth camera, and the fifth camera.

In one or more embodiments, some or all of the cameras in the imagingsystem are co-located (as illustrated in FIG. 2). The co-located camerasmay reside directly adjacent to one another (e.g., abut one another), orthey may be separated by a short distance (e.g., between about 1 cm andabout 5 cm, between about 1 cm and about 10 cm, or between about 1 cmand about 100 cm). In one or more embodiments, the co-located camerasmay be stored in a cabinet or other housing to minimize the overallfootprint of the imaging system, to conceal the imaging system, tocontrol the temperature affecting the imaging system, and/or to protectthe imaging system from tampering and environmental damage. The cabinetmay include a computer-controlled retractable cover, a positive-pressureventilation system, and/or other elements to minimize the occurrence ofdust or other contaminants landing on the camera lenses.

In one or more embodiments, some or all of the cameras in the imagingsystem may be geographically or spatially dispersed (i.e., notco-located). The plurality of cameras may be geographically or spatiallyseparated, for example, by at least 3 meters, by at least 10 meters, byat least 100 meters, by at least a kilometer, or by hundreds orthousands of kilometers (e.g., dispersed throughout the United States orthe world). When geographically or spatially separated, the cameras maybe communicatively coupled to the computing device by a communicationsnetwork (e.g., a local area network, a wide area network, or a mobile orother satellite-based network, any of which may be private or public).In one or more embodiments, the geographically or spatially separatedcameras of the imaging system may be individually controlled bydifferent people or entities and, through coordinated targeting, theimaging system may access (i.e., effectively “crowdsource”) theindividual images captured by the cameras.

Although the exemplary array illustrated in FIG. 2 includes nine cameras101, those of ordinary skill in the art should readily appreciate thatany other plural quantity of cameras 101 are possible in light of, andcontemplated by, the scope of present teachings. Moreover, although theexemplary array illustrated in FIG. 2 includes cameras 101 that areconfigured, adapted, positioned, oriented, arranged, or disposed so asto form a square matrix, those of ordinary skill in the art shouldreadily appreciate that other geometric arrangements or configurationsof the cameras 101 are possible in light of, and contemplated by, thepresent teachings (e.g., non-square rectangular matrices, circulararrangements, semi-circular arrangements, triangular arrangements, orother polygonal or irregular shaped arrangements). The choice ofgeometric arrangement or configuration may depend on numerous factors,including camera design, camera performance, the spatial relationshipbetween individual cameras, the space available to accommodate thecamera array, cost considerations, weight considerations, and otherfactors appreciable by those of ordinary skill in the art in view of thepresent teachings.

The imaging system 101 includes a computing device 105 communicativelycoupled to the cameras 101. The computing device 105 may becommunicatively coupled to the cameras 101 through one or moreintermediate computing devices, such as a switch 102 or othermultiplexing device(s) as illustrated in FIG. 1. The computing device105 is configured to receive or otherwise access the image captured byeach of the cameras 101. The images may be stored locally within memory107 (which may reside in or connect to the cameras 101 or the computingdevice 105), or the images may be stored in a remote storage device,such as a database server and/or web server communicatively coupled tothe computing device 105 by a communications network (e.g., a local areanetwork, a wide area network, or a mobile or other satellite-basednetwork, any of which may be private or public).

The computing device 105 is further configured to co-register theimages. Co-registering the images may include aligning the images suchthat a common reference object occupies the same pixel locations withineach image. Aligning the images may include computing one or morespatial transformations. In one or more embodiments, co-registering theimages includes identifying a common reference object present withineach of the images. When available, multiple common objects may be usedas a reference framework. The common object present within each of theimages may be, for instance, a star or other object in space, while aset of multiple common objects may be some or all of a knownconstellation. Co-registering the images may include determining acoordinate location of the common object within each image.

The coordinate location may be identified through the use of acomputerized coordinate atlas or star catalog, such as the Yale BrightStar catalog, the Henry Draper catalog, the U.S. Naval Observatorycatalog, or any other coordinate atlas or star catalog recognized assuitable by those of ordinary skill in the art. In one or moreembodiments, the computing device 105 may be configured to automaticallydetermine the coordinate location of the common object through the useof such a computerized star catalog and/or star matching software.

In some embodiments, the spatial transformations may be determined “onthe fly” or in real-time for each set of images captured by theplurality of cameras. In one or more embodiments, rather thancalculating the spatial transformations for every set of images, thesame transformations may be used repeatedly (e.g., up until thermaleffects or vibrational issues cause the transformations to becomeinaccurate). In other cases (e.g., those with high thermal anddimensional stability), the transformations may be determined a prioriin a calibration process.

The spatial transformations may include the use of a coordinatetransformation operation or other mathematical operations recognized assuitable by those of ordinary skill in the art. FIG. 4 illustrates, inaccordance with one or more embodiments, an exemplary co-registrationoperation using a coordinate operation. As illustrated in FIG. 4, thetop and bottom graphics each depict a point representing a targetobject, 401 a, 401 b (e.g., a star, a manmade object such as asatellite, or any other object in space). In the top image, the targetobject 401 a is located at position {X_(j), Y_(j)}. In the bottom image,the target object 401 b is located at {X′_(j), Y′_(j)}. The positionsmay be related by the following polynomials handling translation,rotation, and scaling:

X_(j)^(′) = f(X_(j), Y_(j)) = a₁ + a₂X_(j) + a₃Y_(j) + a₄X_(j)Y_(j) + a₅X_(j)² + a₆Y_(j)² + a₇X_(j)²Y_(j) + a₈X_(j)Y_(j)² + a₉X_(j)³ + a₁₀Y_(j)³, Y_(j)^(′) = g(X_(j), Y_(j)) = b₁ + b₂X_(j) + b₃Y_(j) + b₄X_(j)Y_(j) + b₅X_(j)² + b₆Y_(j)² + b₇X_(j)²Y_(j) + b₈X_(j)Y_(j)² + b₉X_(j)³ + b₁₀Y_(j)³.The above polynomials may be written in the following matrix notation:

${\overset{\_}{X^{\prime}} = {\overset{\_}{\overset{\_}{F}}\mspace{11mu}\overset{\_}{a}}},{and}$$\overset{\_}{Y^{\prime}} = {\overset{\_}{\overset{\_}{F}}\mspace{11mu}{\overset{\_}{b}.\mspace{14mu}{where}}}$$\overset{\_}{X^{\prime}} = {{\left\lceil \begin{matrix}X_{1}^{\prime} \\X_{2}^{\prime} \\\vdots \\X_{N}^{\prime}\end{matrix} \right\rceil\mspace{31mu}\overset{\_}{a}} = {{\left\lceil \begin{matrix}a_{1} \\a_{2} \\\vdots \\a_{n}\end{matrix} \right\rceil\mspace{20mu}\overset{\_}{\; b}} = {\left\lceil \begin{matrix}b_{1} \\b_{2} \\\vdots \\b_{N}\end{matrix} \right\rceil\mspace{14mu}{and}}}}$$\overset{\_}{\overset{\_}{F}} = \begin{bmatrix}1 & X_{1} & Y_{1} & {X_{1}Y_{1}} & X_{1}^{2} & Y_{1}^{2} & \cdot & Y_{1}^{3} \\1 & X_{2} & Y_{2} & {X_{2}Y_{2}} & X_{2}^{2} & Y_{2}^{2} & \cdot & Y_{2}^{3} \\1 & X_{N} & Y_{N} & {X_{N}Y_{N}} & X_{N}^{2} & Y_{N}^{2} & \cdot & Y_{N}^{3}\end{bmatrix}$The coefficient vectors, ā and b, may be found using linear leastsquares techniques or other mathematical techniques recognized assuitable by those of ordinary skill in the art. As a result, one imagemay be registered to another (i.e., the two images may beco-registered). The foregoing co-registration process may be repeatedfor all of the images captured by each camera 101, which in turnproduces aligned or substantially aligned images that, as discussedbelow, may be combined to achieve at least a √{square root over (N)}times improvement in signal-to-noise ratio. In one or more embodiments,the computing system 100 is configured to identify the field of viewregion 203 that is common among the fields of view of the plurality ofcameras. Identifying the field of view region 203 that is common amongthe fields of view of the plurality of cameras may be based on theco-registration of the images.

In one or more embodiments, the cameras 101 are configured to capturetheir respective images simultaneously. As used in this disclosure, theterm “simultaneously” does not mean that the referenced events mustoccur at exactly the same time, but rather means that the referencedevents occur either at the same time or within a reasonable margin ofvariation or error as appreciated by those of ordinary skill in the art.Capturing the images simultaneously, or as close to simultaneously aspossible given inherent margins of error and other variations, increasesthe effectiveness of combining the co-registered images. As those ofordinary skill in the art will understand and appreciate, otherco-registration methods in addition to those described herein forillustrative purposes may be utilized and are contemplated within thescope of the present teachings.

The computing system 101 is further configured to combine theco-registered images, thereby generating a combined image having agreater signal-to-noise ratio than any of the individual images standingalone. For purposes of this disclosure, the terms “combine,” “combined,”and “combining” mean add, sum, stack, superimpose, fuse, or integratethrough the use of one or more statistical operations, in addition toother meanings understood by those of ordinary skill in the art in viewof the foregoing illustrative meanings and the context in which theterms are used. In one or more embodiments, the signal-to-noise ratio ofsome or all of the combined image (depending on the size of the commonfield of view region) is at least √{square root over (N)} times greaterthan the signal-to-noise ratio of at least one of the individual imagesstanding alone, where N is the quantity of individual images combined.

When detecting photons in a well-designed and calibrated electro-opticalsystem, the dominant source of noise is the fluctuation in the arrivalrate of photons. For a given time interval, the probability of detectingk photons is given by the following Poisson distribution where λ is themean of the distribution:

${{P(k)} = \frac{\lambda^{k}e^{- \lambda}}{k!}},$One property of the Poisson distribution is that the variance is alsogiven by λ and the standard deviation,

is then given by

=√{square root over (λ)}. As illustrated in the following equation, thesignal-to-noise ratio (SNR) is defined to be the mean of the signaldivided by the standard deviation of the variation in the signal:

${SNR} = \frac{\lambda}{\sigma}$For the Poisson distribution, this simply reduces to:

${SNR} = {\frac{\lambda}{\sqrt{\lambda}} = \sqrt{\lambda}}$Combining N samples (e.g., individual images captured by an individualcamera within the common field array), the expected signal will then begiven Nλ and the signal-to-noise ratio will be given by:

${SNR}_{N} = {\frac{N\;\lambda}{\sqrt{N\;\lambda}} = \sqrt{N\;\lambda}}$Thus, the SNR_(N) for N combined images is simply √{square root over(N)} times the SNR, √{square root over (λ)}, of an individual image.

By way of example, in one or more embodiments in which the imagingsystem features four cameras, each of which captures an individual imagewith a signal-to-noise ratio of 10 (generally considered by those ofordinary skill in the art as being of minimal quality), the enhancedsignal-to-noise ratio of some or all of the combined image may bedetermined by the expression 10√{square root over (4)}, or 20(approaching an image considered useful for astronomical images by thoseof ordinary skill in the art). Similarly, in an exemplary embodiment inwhich the imaging system features thirty-six cameras, each of whichcaptures an individual image with a signal-to-noise ratio of 10, theenhanced signal-to-noise ratio of some or all of the combined imageswould be 10√{square root over (36)} or 60 (considered very good by thoseof ordinary skill in the art).

As discussed above, the field of view region 203 that is common amongthe fields of view of the cameras may vary in size depending on thefields of view of the cameras and the manner in which the cameras areconfigured, adapted, positioned, oriented, arranged, or disposed. Thus,the common field of view region 203 may overlap portions of each fieldof view of the individual cameras (e.g., 301 through 304, as illustratedin FIG. 3), or the common field of view region 203 may overlap all orsubstantially all of each field of view of the individual cameras (asillustrated in FIG. 2). In the latter case, a larger portion of theimage captured by each of the individual cameras 101 is enhanced by the√{square root over (N)} signal-to-noise ratio multiplier.

The significant increase in signal-to-noise ratio and resulting imagesensitivity or quality of some or all of the combined image generatedusing the common field array 201 compared to any individual camera 101within the array permits the imaging system 100 to detect and/or imageobjects (e.g., faint objects) in space or other low-light environmentswithout having to rely on expensive, high-performance cameras anddespite the fact that each individual camera 101 in the array 201 is,when taken alone, incapable of detecting and/or imaging such objects.

In one or more embodiments, the imaging system 100 provides separatebenefits in addition to providing enhanced signal-to-noise ratio (e.g.,the ability see dimmer targets). One such additional benefit is theability to harness a substantially increased bit depth and, through theuse of the increased bit depth, collect an amount of data required toperform advanced deconvolution operations that each individual camera101 in the imaging system 100, when taken alone, is incapable ofcollecting due to limitations in bit depth.

Deconvolution is a process that is useful for recovering a pristineimage from a blurred image. Deconvolution is limited, however, by boththe amount of data available in the blurred image and the bit depth ofthe imaging system capturing the blurred image. The bit depth of acamera determines how many discrete intensities of light (e.g., shadesof gray) the camera can resolve. The quantity of resolvable intensitiesincreases according to the equation # resolvable intensities=2^(N),where N is the number of bits, aka the “bit depth” of the image sensorof the camera. Thus, an 8-bit camera can resolve 256 differentintensities of light (i.e., shades of gray) ranging from values of 0,which represents black, to 255. A 16-bit camera can resolve 65,536different intensities of light ranging from values of 0 to 65,535. Inone or more embodiments, the imaging system 100 described herein may, byincreasing the overall available bit depth of an imaging system 100,increase the amount of data that can be collected from a blurred image.The use of one or more embodiments of the imaging system 100 describedherein may, for instance, increase a bit depth from 10 or 14 bit depth(as might be found within the digital camera of an “off the shelf” orconventional telescope considered economical or low-performance by thoseof ordinary skill in the art at the time of the present application'sfiling) to a significantly larger bit depth capable of collecting moreinformation about an unresolved target from a blurred image. Forexample, in one or more embodiments, an array constructed of 1024cameras of 14 bit depth each will result in the ability to resolve thebrightness of the measured image radiance to 16,776,192 individuallevels, which is an effective bit depth of 23.9999, or almostidentically 14 bits plus Log base 2 of the 1024 number in the array, or“10” additional bits of energy resolution.

In one or more embodiments, the imaging system 100 will increase theavailable bit depth according to the equation Log base 2(M*(2{circumflex over ( )}N−1)), where M is the number of cameras 101 inthe array 201 and N is the bit depth of the camera device 101. This bitdepth is slightly less than but almost equal to Log base 2 (M)+N. Thus,the bit depth of the aggregate array 201 is essentially increased by Log2 of the size of the array 201. The imaging system 100 may, forinstance, be configured or adapted to, or otherwise made capable of,natively providing datasets that are 2³⁶ bit depth or of othermagnitudes that have not been achievable with previously existingimaging systems. In one or more embodiments in which the imaging system100 includes four 10-bit cameras 101, the overall or aggregate bit depthmay be increased to approximately 12 bits, i.e., Log base 2 (4×255).Moreover, the ability of the imaging system 100 to increasesignal-to-noise ratio in and of itself further enhances thedeconvolution process because the efficacy of deconvolution increases assignal-to-noise ratio increases.

Having captured an increased amount of data through the use of anincreased overall bit depth, the imaging system 100 may produce an imagecarrying the requisite information needed to perform advanceddeconvolution operations (e.g., blind deconvolution and other methods ofdeconvolution that cannot be performed using cameras 101 that, whentaken alone, are incapable of collecting sufficient data about theunresolved target and/or are considered economical or low-performance bythose of ordinary skill in the art at the time the present applicationis filed). By making advanced deconvolution and other advanced signalprocessing and mathematical operations possible—in addition to theincrease in signal-to-noise ratio described herein—the imaging system100 further increases the probability of recovering a pristine imagefrom a blurred image. Thus, one or more embodiments of the imagingsystem 100 may provide a dramatic improvement in the ability to resolvetargets when compared to previously existing imaging systems or to theindividual cameras 101 of the imaging system 100 when taken alone.

Another separate technological advance and benefit provided by one ormore embodiments of the imaging system 100 (in addition to the provisionof enhanced signal-to-noise ratio) is a reduction in image artifactscaused by cosmic rays. When using a single camera with a highsignal-to-noise ratio (e.g., a single shot-noise-limited camerarecognized by those of ordinary skill in the art as astronomy-grade andhaving high image sensitivity), an artifact may appear in a capturedimage if a cosmic ray happened to penetrate the focal plane of thecamera at the time the image was captured. The artifact may appear, forexample, as a streak. When conducting deep space target searching orotherwise attempting to detect and/or image objects (e.g., faintobjects), such artifacts can make it difficult to confirm the truepresence of a target object. In one or more embodiments of the imagingsystem 100 described herein, the effect of such cosmic rays is greatlyreduced by virtue of the common field of view region 201. Namely, acosmic ray would need to simultaneously penetrate the focal plane of amajority or all of the cameras 101 of the imaging systems 100 describedherein in order to remain visible after the individual images capturedby the cameras 101 are combined. The probability of such an occurrenceis extremely low. Where a cosmic ray penetrates the focal plane of onlya single camera 101 within the imaging system 100, on the other hand,any artifact produced within the single image captured by that singlecamera 101 will effectively be “washed out” or outweighed once combinedwith the images captured by the other cameras 101 in the imaging system100.

Hybrid Field Array

FIG. 5 illustrates, in accordance with one or more embodiments, a hybridfield array 500 of an exemplary imaging system 100. As illustrated inFIG. 5, in one or more embodiments an imaging system 100 includes aplurality of camera arrays 500. Each camera array 510 includes aplurality of cameras 511. Each camera 511 is configured to capture animage and has a field of view defining the boundaries of the capturableimage. Each camera array 510 within the plurality of camera arrays 500has a common field of view region that is common among the fields ofview of the cameras 511 in that respective common field camera array510. In one or more embodiments, the common field of view region of eachrespective camera array 510 may partially overlap the common field ofview region of another (e.g., adjacent or proximate) camera array 510.

The imaging system 100 further includes a computing devicecommunicatively coupled to the plurality of camera arrays 500. Dependingon the required and available processing power, memory, bandwidth, andother computing considerations, in one or more embodiments the imagingsystem 100 may include either a single computing device 105communicatively coupled to each of the camera arrays 510, or a pluralityof computing devices 105 each communicatively coupled to one or more ofthe camera arrays 510. Relatedly, it should be understood andappreciated by those of ordinary skill in the art that descriptions ofvarious functions executable by “the computing device” may be executedby a single computing device, or may be distributed or replicated acrossa plurality of computing devices (e.g., for efficiency, redundancy, orother purposes).

The computing device 105 is configured to generate a combined image fromthe images captured by each of the cameras in each camera array 510(i.e., captured by the plurality of cameras 511 within each camera array510). In one or more embodiments, the combined image from each array 510has a greater signal-to-noise ratio than any of the individual imagescaptured by any individual camera 511 of each respective camera array510 standing alone. As discussed above with respect to the common fieldarray 201, in one or more embodiments the signal-to-noise ratio of someor all of the combined image (depending on the size of the common fieldof view region) is at least √{square root over (N)} times greater thanthe signal-to-noise ratio of at least one of the images standing alone,where N is the quantity of images combined. In one or more embodiments,generating the combined image may include the individual cameras 511within each respective camera array 510 capturing their respectiveimages simultaneously with respect to one another. Generating thecombined image may also include the cameras 511 within the imagingsystem 100 capturing their respective images simultaneously across allof, or a subset of, the plurality of camera arrays 500.

In one or more embodiments, generating the combined image includesco-registering the images captured by the plurality of cameras 511within each respective camera array 510. Generating the combined imagemay also include co-registering the images captured by the plurality ofcameras 511 across all of, or a subset of, the plurality of cameraarrays 500. Co-registering the images may include aligning the imagessuch that the common object occupies the same pixel locations withineach image. Aligning the images may include computing one or morespatial transformations for each image as discussed above with respectto the common field array 201. In one or more embodiments, generatingthe combined image may include identifying the field of view region thatis common among the fields of view of the plurality of cameras of eachrespective camera array. Identifying the field of view region that iscommon among the fields of view of the plurality of cameras 511 of eachrespective camera array 510 may be based on the co-registration of theimages.

Further referring to FIG. 5, in one or more embodiments, the computingdevice 105 is further configured to stitch the plurality of combinedimages together (e.g., to stitch a first combined image from a firstcommon field array 510 a to a second combined image from a second commonfield array 501 b, which itself may be stitched to a third combinedimage from a third common field array 510 c, and so on). The computingdevice 105 may perform the stitching operation based at least in part onthe spatial relationship in which the common field of view region ofeach respective camera array 510 partially overlaps the common field ofview region of another 510 of the plurality of camera arrays 500. Bystitching together the plurality of combined images based on the spatialrelationship in which the common field of view region of each respectivecamera array 510 partially overlaps the common field of view region ofanother 510 of the plurality of camera arrays 500, the computing device105 may generate a stitched or composite image representing a widerfield of view than any combined image standing alone. Moreover, bycombining the images captured within the field of view region that iscommon among the fields of view of the plurality of cameras 511 of eachrespective camera array 510, some or all of the composite image may havea greater signal-to-noise ratio than any of the individual imagescaptured by individual cameras 511 of each respective camera array 510standing alone.

One or more embodiments of the imaging system 100 described herein maybe particularly beneficial for imaging geosynchronous satellites.Moreover, because the imaging system 100 may be used to generate acomposite image of any desired dimensions by reconfiguring, rearranging,readapting, repositioning, or reorienting the position of the cameraarrays, the imaging system 100 may be configured, arranged, adapted,positioned, oriented, or disposed so as to image a desired “strip” ofspace, such as the known pathway in which a particular satellite orother target object travels (e.g., orbits).

Adaptive Field Array

FIG. 6 illustrates, in accordance with one or more embodiments, anadaptive field array 600 of an exemplary imaging system 100. Asillustrated in FIG. 6, one or more embodiments of an imaging system 100include a plurality of cameras 101. Each camera 101 is configured tocapture an image and has a field of view defining the boundaries of thecapturable image. The cameras 101 may be configured, arranged, adapted,positioned, oriented, or disposed so as to form a camera array. In oneor more embodiments, the plurality of cameras 101 may be configured,arranged, adapted, positioned, oriented, or disposed in a variety ofspatial configurations and may be reconfigurable, in an automatic,triggered, or otherwise computer-controlled manner, between the varietyof spatial configurations. The plurality of cameras 101 may beconfigured, for instance, in a first configuration 601 in which thefields of view of the cameras 602 collectively form a stitched orcomposite field of view 603 that is greater than any of the fields ofview 602 of any of the individual cameras standing alone. The camerasmay also be configured, for example, in a second configuration 201 inwhich a field of view region 202 is common among the fields of view ofthe cameras and the detected target object is positioned within thecommon field of view region 203.

The imaging system 100 includes a computing device 105 communicativelycoupled to the cameras 101. In one or more embodiments, the computingdevice 105 is configured to detect the target object in at least oneimage captured by one of the cameras 101 and, in response to thedetection, automatically reconfigure, readapt, reposition, reorient, orrearrange (e.g., by physically moving their spatial location withrespect to the target object) some or all of the plurality of cameras101 from the first configuration 601 to the second configuration 201.Once the imaging system has reconfigured some or all of the cameras 101from the first configuration 601 to the second configuration 201, theimaging system 100 may then generate a combined image of the detectedtarget as described above. In one or more embodiments, thesignal-to-noise ratio of some or all of the combined image is at least√{square root over (N)} times greater than the signal-to-noise ratio ofat least one of the individual images standing alone, where N is thequantity of individual images combined. The reconfiguration, readapting,repositioning, reorientation, or the rearrangement can be likened to asea-anemone's actions. When the anemone is in “target” (aka “food”)detection mode, the arms of the anemone are detecting equally in nearlyevery direction, spread out over many angles, so as to increase itsvolume, thereby increasing the likelihood of detecting the presence offood. Once a detection occurs, the sensors, i.e., the anemone's arms,then become exquisitely focused on the target, and all or most of thearms converge upon the food. Likewise, in one or more embodiments, thecameras may reconfigure, readapt, reposition, reorient, or rearrange.This may be achieved by mounting each camera, or each sub-array, onto adynamic, computer-controlled mount, such as a common “Pan/Tilt” cameramount, or a telescope gimbal system. These are intelligently controlledto either focus intently, in the common field array mode, on a specificlocation of interest, or to be spread out in many directions, mosaicallysensing a greater FOV, at lesser sensitivity, than when it is intentlyfocusing its attention in the common field array mode.

The imaging system 100 may thus be adaptive, dynamic, or responsive inthe sense that it may initially harness the benefit of a stitched fieldcamera array 601 (e.g., a wide composite field of view that, while beingsuboptimal for capturing images of useful image quality, may be usefulfor at least initially detecting the possible presence of a targetobject) and then automatically adapt or reconfigure the camera arrayinto a common field camera array 201 in accordance with the one or moreembodiments described herein so as to provide a √{square root over (N)}times improvement in signal-to-noise ratio over any individual imagecaptured by any of the individual cameras within the array. In one ormore embodiments, the imaging system 100 may manually or automaticallyreconfigure the array back and forth between the first configuration 601and the second configuration position 201 as needed or at a desiredpredetermined interval based on the imaging objective. Although theforegoing description provides an example in which the camera array ofthe imaging system 100 is reconfigurable between a first position 601and a second position 201, those of ordinary skill in the art should,based on such description, understand and appreciate that the array maybe configured, arranged, adapted, positioned, oriented, or disposed in avirtually limitless number of different spatial configurations (e.g., athird configuration, a fourth configuration, a fifth configuration, andthe like) and may be reconfigurable, in an automatic, triggered, orotherwise computer-controlled manner, between some or all of the varietyof spatial configurations.

Identification of Uncorrelated Target

Camera Calibration

Image processing performed by one or more of the embodiments of theimaging system 100 in the context of identifying a faint space object asan uncorrelated target is described. FIG. 7 describes an exemplarycamera calibration process 700 which includes any or all of the stepsdescribed below. In this example, Cameras 1 through 4 (701 through 704)are being used, where Camera 1 (701) is identified as a Master. In oneor more embodiments, all cameras have identical optics, therefore allcameras have identical fields of view (FOV).

Dark Frame Calibration (Laboratory)

In the dark frame calibration step 710, the camera lenses are covered,and the dark frames are collected with each of the cameras 701 through704 at varying exposures and temperatures. Hot pixels are then detectedand logged. The camera/lens responsivity data is used to develop acamera performance model showing image noise versus temperature andexposure length. The responsivity model may be incorporated into theoverall system software and applied to actual data frames collected.

Flat Frame Calibration (Laboratory)

In the flat frame calibration step 720, the camera lenses are coveredwith uniform, translucent filters (e.g., opal glass). The cameras 701through 704 are then illuminated with a uniform light source. The imageframes are then collected with varying exposure. A sensorresponse/responsivity fall-off model is then developed using thecollected frames, which may provide a detailed model of image intensityfall-off as a function of distance from the optical axis.

Bias Frame Calibration (Laboratory)

In the bias frame calibration step 730, the camera lenses are coveredwith a cap, and the cameras 701 through 704 are placed in a darkenvironment. A shortest possible exposure time is preferably used. Therandom sensor noise of the cameras 701 through 704 due to on-chipamplifiers is then detected, and preferably mapped with a median ofabout 5-10 bias frames on the average.

Barrel Distortion Calibration (laboratory)

In the barrel distortion calibration step 740, the grid-marked testscreen is imaged with a wide field optics in order to collect an imagewith a distortion field created by the lens. The distortion modelconstants that may correct the image field warping into a flat image arethen developed. A pixel-by-pixel map of the image frame relative to theperfect image may also be developed.

Field Calibration

In the field calibration step 750, the sky is imaged with the cameras701 through 704, collecting a star layout image to be used as a mask(Sky Cal Image).

Data Collection

FIG. 8 describes an exemplary data collection process 800 as describedbelow.

Star Matching

A search and detection algorithm is used in the star matching step 810to identify stars/star constellation patterns in an image 801. Thisincludes pixel mapping 820 and image registration 830. In the pixelmapping 820, an optical axis pointing to the orientation in the rightascension/declination (sky coordinates) is determined using one or moreidentified star patterns. In one or more embodiments, each sensor pixelof each camera's focal plane sensor is mapped to its own unique rightascension/declination. In the image registration 830, the cameras 1-N(where N=4 in this example) that have been calibrated are registered.The image frames are mapped with one another using the identified rightascension/declination. Each set of a plurality of images 801-804, whichare coincident in time and imaging the same or substantially the samearea, are registered (aligned) with each other using the stars in theimage as reference points.

Image Combine

The individual corresponding pixel values of each registered frame aresummed to create the combined image 850 in the image combine step 840.As previously discussed, this addition raises the signal-to-noise ratioof the image by a factor of approximately N^(1/2), or √{square root over(N)}. Further, this frame addition essentially amplifies the detectioncapability of the imaging system 100, allowing it to detect dimmerobjects collectively, increasing the sensitivity relative to that of asingle camera 101. Dimmer objects include smaller objects, or objects ata greater altitude (distance).

Data Reduction

FIG. 9 describes an exemplary data reduction process 900 as describedbelow.

Aggregate Addition and Streak Detection

In the aggregate addition and streak detection step 910, the first image901 n of a series of captured, combined images 901 is used as a mask toremove stars and other noise sources. Then a number of sequentialcaptured images are registered and aggregated while subtracting the maskimage to remove stars. Streaks in image(s) are caused by, e.g., asolar-illuminated space object traveling in orbit during image exposure.The stars appear as essentially points of light, as they are moving veryslowly relative to the imaging system 100. The cameras' exposure time isshort relative to the stars' motion, while it is long relative to theobject's motion. For instance, satellites travel at high angular rates,and on paths typically different from a star's motion. Thus, streaks maybe detected by performing an image processing technique 915, such asHough transform. Hough transform is similar to Radon transform. Radontransform is typically used in processing imagery from an X-ray basedcomputerized axial tomography (CAT) scan, such as one that may be usedin medical diagnostic procedures.

Hough transform provides a mathematics-based method to examine imageryand detect streaks, and it may be implemented by software means. Houghtransform can provide streak location on the image and its length. Thisin turn provides streak start and end points. With start and end pointsand a point in the middle, there now exist three time-tagged points(each having a right ascension and declination coordinate). Thesetime-tagged points may be used to perform Initial Orbit Determination(IOD).

Streak Characterization

In the streak characterization step 920, using the detected streakinformation, an orbital path relative to mapped image pixels isconstructed for the streak using some or all of the following variables:

-   Start time: to detect when the streak begins in the aggregate image-   Length: to detect the total length of the streak and the time of the    travel indicated by the streak-   RA/DEC/Time: to develop the right ascension/declination and time    history of a streak-   Average pixel counts: an average value of pixels in a streak-   Integrated pixel counts: a summed value of pixels in a streak over    the length of the streak    Accumulator and Correlator

The accumulator and correlator functions 930 operate to accumulate andcorrelate the streak data in successive aggregations. As a result, thefull streak extent is constructed, including such information as thestart and stop times, start and stop right ascension/declination, andtime vs the right ascension/declination of the imaged object.

Orbit Determination (IOD)

Using the streak time and right ascension/declination data, the orbitparameters are mathematically calculated in the orbit determination step940. IOD provides an initial estimate of the orbital elements for theobject. These elements (values) uniquely describe the orbit shape, size,orientation in inertial space, and object location in the orbit at thetime of the estimate (called the “epoch”). Some of the orbital elementsare virtually unchanging due to physics, such as the orbit shape(eccentricity) and the orbit inclination (angle of orbit plane relativeto earth's equator). Orbital elements may be used to predict theobject's position at a future time. Orbital elements may also be used togenerate derived parameters, such as the mean motion (how many orbitalrevolutions per day), and the true anomaly (where the object is in theorbit). The derived parameters may be used to compare the detectedobject's orbit to the orbits of objects in the current space catalog.

Catalog Search

In the catalog search step 950, using the previously determined orbitalparameters above, a star chart such as the US Strategic Command spaceobject catalog is searched. This is done to determine if the orbitparameters of the objects currently in the catalog match the orbitparameters derived from the detected streak (step 960). This process maybe simplified to a manageable size by using the derived elements. Forexample, with respect to the mean motion, it is possible to compare onlythe detected object's orbit with the orbits of cataloged objects havinga mean motion within +/−5% of the detected object's mean motion.Additional steps may be used to further reduce the search space. Themetrics or measurement of goodness of the match is reported, if apositive match is found (step 970). The detected object is correlated,and no further action is necessary. If no match occurs within theuser-established match criteria, then the imaged object is identified asan uncorrelated target (i.e., not in catalog) (step 980), and the orbitdata (ephemeris/state vector) is further provided for use by a narrowfield of view sensors for any follow-up (step 990), which may beperformed separately by a large aperture, narrow FOV space network assetto identify the object.

The foregoing description is presented for purposes of illustration. Itis not intended to be exhaustive or to limit the subject matter to theprecise forms disclosed. Those of ordinary skill in the art will readilyrecognize and appreciate that modifications and variations are possiblein light of, and as contemplated by, the present teachings. Thedescribed embodiments were chosen in order to best explain theprinciples of the subject matter, its practical application, and toenable others skilled in the art to make use of the same in variousembodiments and with various modifications as are best suited for theparticular application being contemplated.

What is claimed is:
 1. An imaging system, comprising: a plurality ofcameras, each camera being configured to capture an image substantiallysimultaneously with respect to one another, and having a field of viewdefining boundaries of the captured image, wherein the plurality ofcameras are configured such that a field of view region is common amongthe fields of view of the plurality of cameras; a computing devicecommunicatively coupled to the cameras, the computing device configuredto: access one or more of the images captured by the plurality of thecameras, co-register the accessed images, and combine the co-registeredimages, thereby generating a combined image with at least one portionhaving a greater signal-to-noise ratio than any of the images standingalone; and a synchronized timecode reader/generator comprising a globalpositioning system (GPS) receiver coupled to the computing device, thesynchronized timecode reader/generator being configured to providecoordinate and timing data to the computing device for purposes offacilitating co-registration of the accessed images based on a time eachimage was captured, and being configured to support resolution values ofabout 10 milliseconds or fewer.
 2. The imaging system of claim 1,wherein co-registering the accessed images includes identifying a commonobject present within each of the one or more of the images.
 3. Theimaging system of claim 2, wherein co-registering the accessed imagesincludes determining within each accessed image a set of spatialcoordinates of the common object.
 4. The imaging system of claim 2,wherein co-registering the accessed images includes aligning theaccessed images such that the common object occupies the same pixellocations within each accessed image.
 5. The imaging system of claim 4,wherein aligning the accessed images includes computing one or morespatial transformations for each accessed image.
 6. The imaging systemof claim 1, wherein a signal-to-noise ratio of some or all of thecombined image is at least √{square root over (N)} times greater than asignal-to-noise ratio of at least one of the images standing alone,where N is a quantity of images combined.
 7. The imaging system of claim1, wherein the plurality of cameras includes at least four cameras andthe field of view region is common among at least four of the fields ofview of the plurality of cameras.
 8. The imaging system of claim 1,wherein the plurality of cameras includes at least five cameras and thefield of view region is common among at least five of the fields of viewof the plurality of cameras.
 9. The imaging system of claim 1, whereinthe plurality of cameras includes at least six cameras and the field ofview region is common among at least six of the fields of view of theplurality of cameras.
 10. The imaging system of claim 1, wherein thefield of view region is common among all of the fields of view of theplurality of cameras.
 11. The imaging system of claim 1, wherein thecomputing device is further configured to identify the field of viewregion that is common among the fields of view of the plurality ofcameras.
 12. The imaging system of claim 11, wherein the identificationof the field of view region that is common among the fields of view ofthe plurality of cameras is based on the co-registration of the accessedimages.
 13. The imaging system of claim 1, wherein the plurality ofcameras includes: a first camera having a first field of view; a secondcamera having a second field of view; and a third camera having a thirdfield of view.
 14. The imaging system of claim 13, wherein the pluralityof cameras are configured such that: at least a portion of the firstfield of view of the first camera defines a same spatial region as aportion of the second field of view of the second camera, therebycreating a field of view region that is common between the first cameraand the second camera; and at least a portion of the field of view ofthe third camera defines the same spatial region as the field of viewregion that is common between the first camera and the second camera,thereby creating a field of view region that is common between the firstcamera, the second camera, and the third camera.
 15. The imaging systemof claim 14, wherein the plurality of cameras further includes a fourthcamera having a fourth field of view.
 16. The imaging system of claim15, wherein the plurality of cameras are further configured such that atleast a portion of the fourth field of view of the fourth camera definesthe same spatial region as the field of view region that is commonbetween the first camera, the second camera, and the third camera,thereby creating a field of view region that is common between the firstcamera, the second camera, the third camera, and the fourth camera. 17.The imaging system of claim 16, wherein the plurality of cameras furtherincludes a fifth camera having a fifth field of view.
 18. The imagingsystem of claim 17, wherein the plurality of cameras are furtherconfigured such that at least a portion of the fifth field of view ofthe fifth camera defines the same spatial region as the field of viewregion that is common between the first camera, the second camera, thethird camera, and the fourth camera, thereby creating a field of viewregion that is common between the first camera, the second camera, thethird camera, the fourth camera, and the fifth camera.
 19. The imagingsystem of claim 1, wherein the plurality of cameras are co-located. 20.The imaging system of claim 1, wherein the plurality of cameras aredispersed.
 21. The imaging system of claim 1, wherein the plurality ofcameras are communicatively coupled to the computing device by awireless network.
 22. An imaging system, comprising: a plurality ofcamera arrays, each camera array including a plurality of cameras eachcamera configured to capture an image substantially simultaneously withrespect to one another and having a field of view defining boundaries ofthe captured image, and each camera array having a common field of viewregion that is common among the fields of view of the plurality ofcameras of each respective camera array, wherein the common field ofview region of each respective camera array partially overlaps thecommon field of view region of another of the camera arrays; a computingdevice communicatively coupled to the camera arrays, the computingdevice configured to: generate from the captured images of each cameraarray a combined image having a greater signal-to-noise ratio than anyof the images of each respective camera array standing alone, and stitchthe combined images together based on a spatial relationship in whichthe common field of view region of each respective camera arraypartially overlaps the common field of view region of another of thecamera arrays, thereby generating a composite image representing a widerfield of view than any combined image standing alone and having agreater signal-to-noise ratio than any of the images captured by any ofthe cameras of each respective camera array standing alone; and asynchronized timecode reader/generator, including a global positioningsystem (GPS) receiver coupled to the computing device, the synchronizedtimecode reader/generator being configured to provide coordinate andtiming data to the computing device for purposes of facilitatingco-registration of the accessed images based on a time each image wascaptured, and being configured to support resolution values of about 10milliseconds or fewer.
 23. An imaging system, comprising: a plurality ofcameras, each camera configured to capture an image substantiallysimultaneously with respect to one another and having a field of viewdefining boundaries of the captured image, and the plurality of camerasconfigured such that the fields of view of the cameras collectively forma composite field of view that is greater than any of the fields of viewof any of the cameras standing alone; a computing device communicativelycoupled to the cameras, the computing device configured to: detect atarget object in an image captured by one of the cameras, andautomatically reconfigure, readapt, reposition, reorient, or rearrangethe plurality of cameras such that a common field of view region iscommon among the fields of view of the plurality of cameras and thedetected target object is positioned within the common field of viewregion, access an image captured by each of the cameras after beingreconfigured, readapted, repositioned, reoriented, or rearranged,co-register the images, and combine the images, thereby generating acombined image with at least one portion having a greatersignal-to-noise ratio than any of the images standing alone; and asynchronized timecode reader/generator comprising a global positioningsystem (GPS) receiver coupled to the computing device, the synchronizedtimecode reader/generator being configured to provide coordinate andtiming data to the computing device for purposes of facilitatingco-registration of the accessed images based on a time each image wascaptured, and being configured to support resolution values of about 10milliseconds or fewer.